This page showcase visualisations of spatial Transcriptomics, scATAC-seq Track Plot, and Multi-omics Visualisation with ShinyCell2. Please refer to the corresponding session for more detailed explanations of the features and the visual examples for demonstrations.
This view is for detailed exploration of spatial gene expression with metadata overlaid, allowing users to focus on specific regions of the tissue images.
🧭 Step-by-Step Guide
🚀 How It Works
This tab uses a zooming feature powered by restricting the viewpoint
of a ggplot2 object. This technique allows:
Use this view when you want to compare different data types directly within the spatial tissue context.
🧭 Step-by-Step Guide
For example, one can compare cell type annotations with gene expression patterns (see below). This layout helps in understanding how gene expression relates to specific cell types within the tissue architecture.
With these tools, you can intuitively explore spatial transcriptomics data, uncovering detailed spatial patterns and relationships in your tissue samples.
ShinyCell2 makes it easy to visualize scATAC-seq data using IGV-style locus plots powered by the Trackplot function.
🧭 Step-by-Step Guide
Optionally add annotation tracks (e.g., ENCODE cis-regulatory elements) in standard BED format for extra context.
🚀 How It Works
Trackplot uses bwtool (a fast bigWig query tool) and data.table
in R for performance. It extracts open chromatin signals directly from
bigWig files, making it over 15× faster than typical plotting tools, and
ideal for large datasets or plotting multiple regions.
With this setup, we can achieve fast, flexible, and informative chromatin accessibility visualizations, enriched with regulatory annotations.
ShinyCell2 makes it easy to explore multi-omics single-cell data—including RNA, protein, and chromatin accessibility—within a single interface.
🧭 Step-by-Step Guide
🚀 Why It’s Powerful
This unified approach gives you a more comprehensive view and
understanding of your single-cell data.
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